import warnings
warnings.filterwarnings('ignore')
import pandas as pd #library for data manipulation and analysis
import numpy as np #library used for working with arrays
import matplotlib.pyplot as plt #library for plots and visualizations
import seaborn as sns #library for visualizations
from scipy.stats import norm #using normal distibutions
from scipy.stats import zscore

#Linear Regression
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split # Sklearn package's randomized data splitting function

#DEsision Tree
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn import metrics

#Random Forest
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn import metrics
from sklearn.ensemble import BaggingClassifier, RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score
import scipy.stats as stats
from sklearn import metrics
from sklearn import tree
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score


#To install xgboost library use - !pip install xgboost 
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier



%matplotlib inline

pd.set_option('display.float_format', lambda x: '%.2f' % x) # To supress numerical display in scientific notations


import scipy.stats #library contains a number of probibility distributions and statistical functions

df = pd.read_csv('Tourism.csv')

df.head()

	CustomerID	ProdTaken	Age	TypeofContact	CityTier	DurationOfPitch	Occupation	Gender	NumberOfPersonVisiting	NumberOfFollowups	ProductPitched	PreferredPropertyStar	MaritalStatus	NumberOfTrips	Passport	PitchSatisfactionScore	OwnCar	NumberOfChildrenVisiting	Designation	MonthlyIncome
0	200000	1	41.00	Self Enquiry	3	6.00	Salaried	Female	3	3.00	Deluxe	3.00	Single	1.00	1	2	1	0.00	Manager	20993.00
1	200001	0	49.00	Company Invited	1	14.00	Salaried	Male	3	4.00	Deluxe	4.00	Divorced	2.00	0	3	1	2.00	Manager	20130.00
2	200002	1	37.00	Self Enquiry	1	8.00	Free Lancer	Male	3	4.00	Basic	3.00	Single	7.00	1	3	0	0.00	Executive	17090.00
3	200003	0	33.00	Company Invited	1	9.00	Salaried	Female	2	3.00	Basic	3.00	Divorced	2.00	1	5	1	1.00	Executive	17909.00
4	200004	0	NaN	Self Enquiry	1	8.00	Small Business	Male	2	3.00	Basic	4.00	Divorced	1.00	0	5	1	0.00	Executive	18468.00

Data Dictionary

• CustomerID: Unique customer ID
• ProdTaken: Whether the customer has purchased a package or not (0: No, 1: Yes)
• Age: Age of customer
• TypeofContact: How customer was contacted (Company Invited or Self Inquiry)
• CityTier: City tier depends on the development of a city, population, facilities, and living standards. The categories are ordered i.e. Tier 1 > Tier 2 > Tier 3. It's the city the customer lives in.
• DurationOfPitch: Duration of the pitch by a salesperson to the customer
• Occupation: Occupation of customer
• Gender: Gender of customer
• NumberOfPersonVisiting: Total number of persons planning to take the trip with the customer
• NumberOfFollowups: Total number of follow-ups has been done by the salesperson after the sales pitch
• ProductPitched: Product pitched by the salesperson
• PreferredPropertyStar: Preferred hotel property rating by customer
• MaritalStatus: Marital status of customer
• NumberOfTrips: Average number of trips in a year by customer
• Passport: The customer has a passport or not (0: No, 1: Yes)
• PitchSatisfactionScore: Sales pitch satisfaction score
• OwnCar: Whether the customers own a car or not (0: No, 1: Yes)
• NumberOfChildrenVisiting: Total number of children with age less than 5 planning to take the trip with the customer
• Designation: Designation of the customer in the current organization
• MonthlyIncome: Gross monthly income of the customer

Data Overview

df.describe(include='all').T

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
CustomerID	4888.00	NaN	NaN	NaN	202443.50	1411.19	200000.00	201221.75	202443.50	203665.25	204887.00
ProdTaken	4888.00	NaN	NaN	NaN	0.19	0.39	0.00	0.00	0.00	0.00	1.00
Age	4662.00	NaN	NaN	NaN	37.62	9.32	18.00	31.00	36.00	44.00	61.00
TypeofContact	4863	2	Self Enquiry	3444	NaN	NaN	NaN	NaN	NaN	NaN	NaN
CityTier	4888.00	NaN	NaN	NaN	1.65	0.92	1.00	1.00	1.00	3.00	3.00
DurationOfPitch	4637.00	NaN	NaN	NaN	15.49	8.52	5.00	9.00	13.00	20.00	127.00
Occupation	4888	4	Salaried	2368	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Gender	4888	3	Male	2916	NaN	NaN	NaN	NaN	NaN	NaN	NaN
NumberOfPersonVisiting	4888.00	NaN	NaN	NaN	2.91	0.72	1.00	2.00	3.00	3.00	5.00
NumberOfFollowups	4843.00	NaN	NaN	NaN	3.71	1.00	1.00	3.00	4.00	4.00	6.00
ProductPitched	4888	5	Basic	1842	NaN	NaN	NaN	NaN	NaN	NaN	NaN
PreferredPropertyStar	4862.00	NaN	NaN	NaN	3.58	0.80	3.00	3.00	3.00	4.00	5.00
MaritalStatus	4888	4	Married	2340	NaN	NaN	NaN	NaN	NaN	NaN	NaN
NumberOfTrips	4748.00	NaN	NaN	NaN	3.24	1.85	1.00	2.00	3.00	4.00	22.00
Passport	4888.00	NaN	NaN	NaN	0.29	0.45	0.00	0.00	0.00	1.00	1.00
PitchSatisfactionScore	4888.00	NaN	NaN	NaN	3.08	1.37	1.00	2.00	3.00	4.00	5.00
OwnCar	4888.00	NaN	NaN	NaN	0.62	0.49	0.00	0.00	1.00	1.00	1.00
NumberOfChildrenVisiting	4822.00	NaN	NaN	NaN	1.19	0.86	0.00	1.00	1.00	2.00	3.00
Designation	4888	5	Executive	1842	NaN	NaN	NaN	NaN	NaN	NaN	NaN
MonthlyIncome	4655.00	NaN	NaN	NaN	23619.85	5380.70	1000.00	20346.00	22347.00	25571.00	98678.00

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4888 entries, 0 to 4887
Data columns (total 20 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   CustomerID                4888 non-null   int64  
 1   ProdTaken                 4888 non-null   int64  
 2   Age                       4662 non-null   float64
 3   TypeofContact             4863 non-null   object 
 4   CityTier                  4888 non-null   int64  
 5   DurationOfPitch           4637 non-null   float64
 6   Occupation                4888 non-null   object 
 7   Gender                    4888 non-null   object 
 8   NumberOfPersonVisiting    4888 non-null   int64  
 9   NumberOfFollowups         4843 non-null   float64
 10  ProductPitched            4888 non-null   object 
 11  PreferredPropertyStar     4862 non-null   float64
 12  MaritalStatus             4888 non-null   object 
 13  NumberOfTrips             4748 non-null   float64
 14  Passport                  4888 non-null   int64  
 15  PitchSatisfactionScore    4888 non-null   int64  
 16  OwnCar                    4888 non-null   int64  
 17  NumberOfChildrenVisiting  4822 non-null   float64
 18  Designation               4888 non-null   object 
 19  MonthlyIncome             4655 non-null   float64
dtypes: float64(7), int64(7), object(6)
memory usage: 763.9+ KB

#Lets see the count and the percentage of missing values in each column
# selecting the instances where missing value is greater than 0
pd.DataFrame({'Count':df.isnull().sum()[df.isnull().sum()>0],'Percentage':(df.isnull().sum()[df.isnull().sum()>0]/df.shape[0])*100})

	Count	Percentage
Age	226	4.62
TypeofContact	25	0.51
DurationOfPitch	251	5.14
NumberOfFollowups	45	0.92
PreferredPropertyStar	26	0.53
NumberOfTrips	140	2.86
NumberOfChildrenVisiting	66	1.35
MonthlyIncome	233	4.77

df['TypeofContact'].value_counts(dropna=False)

Self Enquiry       3444
Company Invited    1419
NaN                  25
Name: TypeofContact, dtype: int64

df = df.dropna()

pd.DataFrame({'Count':df.isnull().sum()[df.isnull().sum()>0],'Percentage':(df.isnull().sum()[df.isnull().sum()>0]/df.shape[0])*100})

	Count	Percentage

#################################CHECK DUPLICATES
#check for duplicated rows
df.duplicated().sum()

0

df.drop(['CustomerID'],axis=1,inplace=True)

# Defining the function for creating boxplot and hisogram 
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize)  # creating the 2 subplots
    
    sns.boxplot(data=data, x=feature, ax=ax_box2, showmeans=True, color="mediumturquoise")  # boxplot will be created and a star will indicate the mean value of the column
    
    if bins:
      sns.histplot(data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, color="mediumpurple")
    else: 
      sns.histplot(data=data, x=feature, kde=kde, ax=ax_hist2, color="mediumpurple")  # For histogram
    
    ax_hist2.axvline(data[feature].mean(), color="green", linestyle="--")  # Add mean to the histogram
    
    ax_hist2.axvline(data[feature].median(), color="black", linestyle="-")  # Add median to the histogram



columns_list = []

for i in df.columns:
    if df.dtypes[i]!=object:
        columns_list.append(i)

for i in columns_list:
    histogram_boxplot(df,i)  #you can't use this part without the defined functions

############################Treating outliers



# outlier detection using boxplot - UNIVARIATE FIRST
# selecting the numerical columns of data and adding their names in a list 
numeric_columns = ['NumberOfTrips', 'DurationOfPitch', 'MonthlyIncome']
plt.figure(figsize=(15, 12))

for i, variable in enumerate(numeric_columns):
    plt.subplot(4, 4, i + 1)
    plt.boxplot(df[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()


#IQR

# to find the 25th percentile and 75th percentile for the numerical columns.
Q1 = df[numeric_columns].quantile(0.25)
Q3 = df[numeric_columns].quantile(0.75)

IQR = Q3 - Q1                   #Inter Quantile Range (75th percentile - 25th percentile)

lower_whisker = Q1 - 1.5*IQR    #Finding lower and upper bounds for all values. All values outside these bounds are outliers
upper_whisker = Q3 + 1.5*IQR

# Percentage of outliers in each column
((df[numeric_columns] < lower_whisker) | (df[numeric_columns] > upper_whisker)).sum()/df.shape[0]*100

NumberOfTrips     2.59
DurationOfPitch   0.02
MonthlyIncome     5.40
dtype: float64

def treat_outliers(df, col):
    """
    treats outliers in a variable
    col: str, name of the numerical variable
    df: dataframe
    col: name of the column
    """
    Q1 = df[col].quantile(0.25)  # 25th quantile
    Q3 = df[col].quantile(0.75)  # 75th quantile
    IQR = Q3 - Q1                # Inter Quantile Range (75th perentile - 25th percentile)
    lower_whisker = Q1 - 1.5 * IQR
    upper_whisker = Q3 + 1.5 * IQR

    # all the values smaller than lower_whisker will be assigned the value of lower_whisker
    # all the values greater than upper_whisker will be assigned the value of upper_whisker
    # the assignment will be done by using the clip function of NumPy
    df[col] = np.clip(df[col], lower_whisker, upper_whisker)

    return df

#check the outliers

#Treating outliers in Rooms column

df = treat_outliers(df,'NumberOfTrips')
df = treat_outliers(df,'DurationOfPitch')
df = treat_outliers(df,'MonthlyIncome')

# visualizing the column after outlier treatment
# outlier detection using boxplot
# selecting the numerical columns where outliers were treated 
numeric_columns = ['NumberOfTrips', 'DurationOfPitch', 'MonthlyIncome']
plt.figure(figsize=(15, 12))

for i, variable in enumerate(numeric_columns):
    plt.subplot(4, 4, i + 1)
    plt.boxplot(df[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 4128 entries, 0 to 4887
Data columns (total 19 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   ProdTaken                 4128 non-null   int64  
 1   Age                       4128 non-null   float64
 2   TypeofContact             4128 non-null   object 
 3   CityTier                  4128 non-null   int64  
 4   DurationOfPitch           4128 non-null   float64
 5   Occupation                4128 non-null   object 
 6   Gender                    4128 non-null   object 
 7   NumberOfPersonVisiting    4128 non-null   int64  
 8   NumberOfFollowups         4128 non-null   float64
 9   ProductPitched            4128 non-null   object 
 10  PreferredPropertyStar     4128 non-null   float64
 11  MaritalStatus             4128 non-null   object 
 12  NumberOfTrips             4128 non-null   float64
 13  Passport                  4128 non-null   int64  
 14  PitchSatisfactionScore    4128 non-null   int64  
 15  OwnCar                    4128 non-null   int64  
 16  NumberOfChildrenVisiting  4128 non-null   float64
 17  Designation               4128 non-null   object 
 18  MonthlyIncome             4128 non-null   float64
dtypes: float64(7), int64(6), object(6)
memory usage: 645.0+ KB

df.head()

	ProdTaken	Age	TypeofContact	CityTier	DurationOfPitch	Occupation	Gender	NumberOfPersonVisiting	NumberOfFollowups	ProductPitched	PreferredPropertyStar	MaritalStatus	NumberOfTrips	Passport	PitchSatisfactionScore	OwnCar	NumberOfChildrenVisiting	Designation	MonthlyIncome
0	1	41.00	Self Enquiry	3	6.00	Salaried	Female	3	3.00	Deluxe	3.00	Single	1.00	1	2	1	0.00	Manager	20993.00
1	0	49.00	Company Invited	1	14.00	Salaried	Male	3	4.00	Deluxe	4.00	Divorced	2.00	0	3	1	2.00	Manager	20130.00
2	1	37.00	Self Enquiry	1	8.00	Free Lancer	Male	3	4.00	Basic	3.00	Single	7.00	1	3	0	0.00	Executive	17090.00
3	0	33.00	Company Invited	1	9.00	Salaried	Female	2	3.00	Basic	3.00	Divorced	2.00	1	5	1	1.00	Executive	17909.00
5	0	32.00	Company Invited	1	8.00	Salaried	Male	3	3.00	Basic	3.00	Single	1.00	0	5	1	1.00	Executive	18068.00

df.loc[df['Gender'] == 'Fe Male', 'Gender'] = 'Female'
df["Gender"].unique()

array(['Female', 'Male'], dtype=object)

Questions:

1. What is the distribution of the monthly income of customers? Are there any noticeable patterns or outliers in the distribution?
2. Which travel package is pitched the most by the salespersons to the customers?
3. How are the different customer attributes correlated to each other?
4. How does a customer's interest in purchasing the newly introduced travel package vary with their occupation?
5. How does a customer's interest in purchasing the newly introduced travel package vary with their marital status?

sns.histplot(data=df,x='MonthlyIncome',stat='density')
plt.show()

sns.countplot(data=df,x='ProductPitched')
plt.xticks(rotation=90)
plt.show()

sns.catplot(x='Occupation', y='ProdTaken', data=df, kind="bar", height=8, aspect=2)
plt.xticks(rotation=90)
plt.show()

sns.catplot(x='MaritalStatus', y='ProdTaken', data=df, kind="bar", height=8, aspect=2)
plt.xticks(rotation=90)
plt.show()

Observations

1. Monthly income has a bimodal distribution
2. The Basic travel package is pitched the most followed by Deluxe
3. Freelancers seem to be the most interested in taking products Occupation wise.
4. Single people take packages the most followed by Unmarried people.

################################. Create Dummy variables

#below redefining buckets for each case
replaceStruct = {
                "Gender":     {"Female": 1, "Male": 0}
                    }
oneHotCols=["TypeofContact","Occupation","ProductPitched","MaritalStatus", "Designation"]


#reassign the values in the dataframe

df=df.replace(replaceStruct)
df=pd.get_dummies(df, columns=oneHotCols)
df.head(10)

	ProdTaken	Age	CityTier	DurationOfPitch	Gender	NumberOfPersonVisiting	NumberOfFollowups	PreferredPropertyStar	NumberOfTrips	Passport	...	ProductPitched_Super Deluxe	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	MaritalStatus_Unmarried	Designation_AVP	Designation_Executive	Designation_Manager	Designation_Senior Manager	Designation_VP
0	1	41.00	3	6.00	1	3	3.00	3.00	1.00	1	...	0	0	0	1	0	0	0	1	0	0
1	0	49.00	1	14.00	0	3	4.00	4.00	2.00	0	...	0	1	0	0	0	0	0	1	0	0
2	1	37.00	1	8.00	0	3	4.00	3.00	7.00	1	...	0	0	0	1	0	0	1	0	0	0
3	0	33.00	1	9.00	1	2	3.00	3.00	2.00	1	...	0	1	0	0	0	0	1	0	0	0
5	0	32.00	1	8.00	0	3	3.00	3.00	1.00	0	...	0	0	0	1	0	0	1	0	0	0
6	0	59.00	1	9.00	1	2	2.00	5.00	5.00	1	...	0	1	0	0	0	0	1	0	0	0
7	0	30.00	1	30.00	0	3	3.00	3.00	2.00	0	...	0	0	1	0	0	0	1	0	0	0
8	0	38.00	1	29.00	0	2	4.00	3.00	1.00	0	...	0	0	0	0	1	0	0	0	1	0
9	0	36.00	1	33.00	0	3	3.00	3.00	7.00	0	...	0	1	0	0	0	0	0	1	0	0
10	0	35.00	1	22.00	0	2	2.00	4.00	1.00	0	...	0	1	0	0	0	0	1	0	0	0

10 rows × 34 columns

# looking at value counts for non-numeric features

num_to_display = 10  # defining this up here so it's easy to change later if I want
for colname in df.dtypes[df.dtypes == 'object'].index:
    val_counts = df[colname].value_counts(dropna=False)  # i want to see NA counts
    print(val_counts[:num_to_display])
    if len(val_counts) > num_to_display:
        print(f'Only displaying first {num_to_display} of {len(val_counts)} values.')
    print('\n\n') # just for more space between 

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 4128 entries, 0 to 4887
Data columns (total 34 columns):
 #   Column                         Non-Null Count  Dtype  
---  ------                         --------------  -----  
 0   ProdTaken                      4128 non-null   int64  
 1   Age                            4128 non-null   float64
 2   CityTier                       4128 non-null   int64  
 3   DurationOfPitch                4128 non-null   float64
 4   Gender                         4128 non-null   int64  
 5   NumberOfPersonVisiting         4128 non-null   int64  
 6   NumberOfFollowups              4128 non-null   float64
 7   PreferredPropertyStar          4128 non-null   float64
 8   NumberOfTrips                  4128 non-null   float64
 9   Passport                       4128 non-null   int64  
 10  PitchSatisfactionScore         4128 non-null   int64  
 11  OwnCar                         4128 non-null   int64  
 12  NumberOfChildrenVisiting       4128 non-null   float64
 13  MonthlyIncome                  4128 non-null   float64
 14  TypeofContact_Company Invited  4128 non-null   uint8  
 15  TypeofContact_Self Enquiry     4128 non-null   uint8  
 16  Occupation_Free Lancer         4128 non-null   uint8  
 17  Occupation_Large Business      4128 non-null   uint8  
 18  Occupation_Salaried            4128 non-null   uint8  
 19  Occupation_Small Business      4128 non-null   uint8  
 20  ProductPitched_Basic           4128 non-null   uint8  
 21  ProductPitched_Deluxe          4128 non-null   uint8  
 22  ProductPitched_King            4128 non-null   uint8  
 23  ProductPitched_Standard        4128 non-null   uint8  
 24  ProductPitched_Super Deluxe    4128 non-null   uint8  
 25  MaritalStatus_Divorced         4128 non-null   uint8  
 26  MaritalStatus_Married          4128 non-null   uint8  
 27  MaritalStatus_Single           4128 non-null   uint8  
 28  MaritalStatus_Unmarried        4128 non-null   uint8  
 29  Designation_AVP                4128 non-null   uint8  
 30  Designation_Executive          4128 non-null   uint8  
 31  Designation_Manager            4128 non-null   uint8  
 32  Designation_Senior Manager     4128 non-null   uint8  
 33  Designation_VP                 4128 non-null   uint8  
dtypes: float64(7), int64(7), uint8(20)
memory usage: 564.4 KB

# plot the heatmap

corr = df.corr()

plt.figure(figsize=(15, 7))
sns.heatmap(corr, annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")
plt.show()

import pandas as pd
pd.set_option('display.max_rows', None)

# Create correlation matrix
corr_mat = df.corr(method='pearson')

# Drop correlations where the variable names are the same
corr_mat = corr_mat.mask(np.tril(np.ones_like(corr_mat, dtype=bool)))

# Convert correlation matrix to 1-D Series and sort
sorted_mat = corr_mat.unstack().drop_duplicates().sort_values()

# Remove correlations with a value of 1 or null values
sorted_mat = sorted_mat[(sorted_mat != 1) & (~sorted_mat.isnull())]

print(sorted_mat)

TypeofContact_Self Enquiry     TypeofContact_Company Invited   -1.00
Occupation_Small Business      Occupation_Salaried             -0.83
ProductPitched_Basic           MonthlyIncome                   -0.62
Designation_Executive          ProductPitched_Deluxe           -0.58
ProductPitched_Deluxe          ProductPitched_Basic            -0.58
MaritalStatus_Married          MaritalStatus_Divorced          -0.47
MaritalStatus_Unmarried        MaritalStatus_Married           -0.43
MaritalStatus_Single           MaritalStatus_Married           -0.42
ProductPitched_Standard        ProductPitched_Basic            -0.37
Designation_Executive          ProductPitched_Standard         -0.37
ProductPitched_Basic           Age                             -0.37
ProductPitched_Standard        ProductPitched_Deluxe           -0.34
Occupation_Salaried            Occupation_Large Business       -0.31
Occupation_Small Business      Occupation_Large Business       -0.27
ProductPitched_Basic           CityTier                        -0.26
MaritalStatus_Unmarried        MaritalStatus_Divorced          -0.22
MaritalStatus_Single           MaritalStatus_Divorced          -0.21
ProductPitched_Super Deluxe    ProductPitched_Basic            -0.20
Designation_Executive          ProductPitched_Super Deluxe     -0.20
MaritalStatus_Unmarried        MaritalStatus_Single            -0.20
ProductPitched_Super Deluxe    ProductPitched_Deluxe           -0.18
Designation_Manager            ProductPitched_Super Deluxe     -0.18
MaritalStatus_Single           MonthlyIncome                   -0.17
Designation_Manager            MaritalStatus_Single            -0.16
MaritalStatus_Single           ProductPitched_Deluxe           -0.16
Age                            ProdTaken                       -0.15
Occupation_Salaried            CityTier                        -0.15
MaritalStatus_Single           NumberOfPersonVisiting          -0.15
ProductPitched_Deluxe          ProdTaken                       -0.14
MonthlyIncome                  ProdTaken                       -0.14
ProductPitched_King            ProductPitched_Basic            -0.13
                               NumberOfPersonVisiting          -0.13
MaritalStatus_Married          ProdTaken                       -0.12
MaritalStatus_Single           ProductPitched_Standard         -0.12
Designation_Senior Manager     ProductPitched_Super Deluxe     -0.12
ProductPitched_Super Deluxe    ProductPitched_Standard         -0.12
ProductPitched_King            ProductPitched_Deluxe           -0.12
MaritalStatus_Unmarried        ProductPitched_Basic            -0.11
MaritalStatus_Single           Age                             -0.11
ProductPitched_Super Deluxe    NumberOfPersonVisiting          -0.10
MaritalStatus_Single           NumberOfChildrenVisiting        -0.10
MaritalStatus_Unmarried        ProductPitched_Super Deluxe     -0.10
ProductPitched_Super Deluxe    NumberOfFollowups               -0.08
MaritalStatus_Single           NumberOfTrips                   -0.08
ProductPitched_Super Deluxe    NumberOfChildrenVisiting        -0.08
                               ProdTaken                       -0.08
ProductPitched_Standard        ProductPitched_King             -0.07
MaritalStatus_Divorced         ProdTaken                       -0.07
MaritalStatus_Unmarried        ProductPitched_King             -0.07
ProductPitched_King            DurationOfPitch                 -0.07
                               NumberOfChildrenVisiting        -0.07
ProductPitched_Basic           Occupation_Small Business       -0.07
                               NumberOfTrips                   -0.07
ProductPitched_King            NumberOfFollowups               -0.06
TypeofContact_Self Enquiry     ProdTaken                       -0.06
Designation_Executive          MaritalStatus_Married           -0.05
MaritalStatus_Married          ProductPitched_Basic            -0.05
ProductPitched_Basic           OwnCar                          -0.05
Occupation_Salaried            DurationOfPitch                 -0.05
Occupation_Small Business      PitchSatisfactionScore          -0.05
ProductPitched_Basic           DurationOfPitch                 -0.04
ProductPitched_Deluxe          Occupation_Large Business       -0.04
MaritalStatus_Single           DurationOfPitch                 -0.04
ProductPitched_Basic           Gender                          -0.04
MaritalStatus_Single           CityTier                        -0.04
ProductPitched_King            ProdTaken                       -0.04
MaritalStatus_Married          Gender                          -0.04
ProductPitched_King            CityTier                        -0.04
ProductPitched_Super Deluxe    ProductPitched_King             -0.04
MaritalStatus_Single           NumberOfFollowups               -0.04
MaritalStatus_Married          NumberOfFollowups               -0.04
ProductPitched_Deluxe          PitchSatisfactionScore          -0.04
Occupation_Small Business      OwnCar                          -0.04
Occupation_Large Business      TypeofContact_Company Invited   -0.04
ProductPitched_Standard        ProdTaken                       -0.04
Occupation_Salaried            TypeofContact_Self Enquiry      -0.03
MaritalStatus_Unmarried        Age                             -0.03
Occupation_Salaried            ProdTaken                       -0.03
MaritalStatus_Divorced         TypeofContact_Company Invited   -0.03
ProductPitched_King            TypeofContact_Company Invited   -0.03
TypeofContact_Company Invited  PreferredPropertyStar           -0.03
PreferredPropertyStar          Age                             -0.03
NumberOfChildrenVisiting       Age                             -0.03
MaritalStatus_Married          CityTier                        -0.03
                               PitchSatisfactionScore          -0.03
PitchSatisfactionScore         CityTier                        -0.03
Gender                         ProdTaken                       -0.03
Occupation_Free Lancer         OwnCar                          -0.03
ProductPitched_King            PreferredPropertyStar           -0.03
OwnCar                         Passport                        -0.03
Occupation_Large Business      DurationOfPitch                 -0.03
Occupation_Salaried            PreferredPropertyStar           -0.03
ProductPitched_Standard        Occupation_Salaried             -0.03
ProductPitched_King            NumberOfTrips                   -0.03
ProductPitched_Standard        NumberOfChildrenVisiting        -0.03
NumberOfFollowups              Age                             -0.03
MaritalStatus_Divorced         NumberOfFollowups               -0.03
MaritalStatus_Unmarried        Passport                        -0.02
ProductPitched_Super Deluxe    Occupation_Large Business       -0.02
MaritalStatus_Single           OwnCar                          -0.02
NumberOfPersonVisiting         Age                             -0.02
ProductPitched_Deluxe          MonthlyIncome                   -0.02
TypeofContact_Self Enquiry     NumberOfFollowups               -0.02
                               PitchSatisfactionScore          -0.02
Occupation_Salaried            MonthlyIncome                   -0.02
ProductPitched_Deluxe          Occupation_Salaried             -0.02
Occupation_Large Business      Age                             -0.02
ProductPitched_Deluxe          TypeofContact_Self Enquiry      -0.02
MaritalStatus_Divorced         ProductPitched_Basic            -0.02
OwnCar                         NumberOfTrips                   -0.02
Occupation_Free Lancer         MonthlyIncome                   -0.02
Occupation_Salaried            Occupation_Free Lancer          -0.02
MaritalStatus_Married          Occupation_Free Lancer          -0.02
ProductPitched_Deluxe          PreferredPropertyStar           -0.02
ProductPitched_Standard        NumberOfPersonVisiting          -0.02
Occupation_Large Business      MonthlyIncome                   -0.02
PitchSatisfactionScore         PreferredPropertyStar           -0.02
ProductPitched_King            Occupation_Large Business       -0.02
Occupation_Free Lancer         DurationOfPitch                 -0.02
Occupation_Small Business      Occupation_Free Lancer          -0.02
MaritalStatus_Unmarried        TypeofContact_Self Enquiry      -0.02
ProductPitched_King            Gender                          -0.02
Occupation_Free Lancer         NumberOfChildrenVisiting        -0.02
                               Gender                          -0.02
PreferredPropertyStar          NumberOfFollowups               -0.02
MaritalStatus_Married          Occupation_Large Business       -0.02
PitchSatisfactionScore         NumberOfPersonVisiting          -0.02
NumberOfChildrenVisiting       Gender                          -0.02
TypeofContact_Self Enquiry     DurationOfPitch                 -0.02
PreferredPropertyStar          CityTier                        -0.02
Occupation_Free Lancer         PreferredPropertyStar           -0.02
ProductPitched_King            Passport                        -0.02
ProductPitched_Deluxe          Occupation_Free Lancer          -0.02
Occupation_Free Lancer         CityTier                        -0.02
ProductPitched_Basic           TypeofContact_Company Invited   -0.02
Occupation_Small Business      ProdTaken                       -0.02
ProductPitched_Deluxe          Passport                        -0.01
Occupation_Free Lancer         TypeofContact_Company Invited   -0.01
MaritalStatus_Unmarried        Occupation_Small Business       -0.01
ProductPitched_Super Deluxe    PreferredPropertyStar           -0.01
ProductPitched_Standard        Passport                        -0.01
ProductPitched_Deluxe          OwnCar                          -0.01
MaritalStatus_Single           Occupation_Salaried             -0.01
Occupation_Salaried            NumberOfTrips                   -0.01
Occupation_Small Business      TypeofContact_Company Invited   -0.01
Occupation_Large Business      CityTier                        -0.01
TypeofContact_Company Invited  NumberOfTrips                   -0.01
                               Age                             -0.01
MaritalStatus_Unmarried        PitchSatisfactionScore          -0.01
MaritalStatus_Divorced         Occupation_Free Lancer          -0.01
MaritalStatus_Single           PreferredPropertyStar           -0.01
ProductPitched_Standard        Occupation_Free Lancer          -0.01
NumberOfTrips                  CityTier                        -0.01
Occupation_Salaried            Passport                        -0.01
MaritalStatus_Unmarried        Occupation_Free Lancer          -0.01
MaritalStatus_Divorced         NumberOfChildrenVisiting        -0.01
Occupation_Salaried            NumberOfChildrenVisiting        -0.01
Occupation_Small Business      NumberOfFollowups               -0.01
OwnCar                         ProdTaken                       -0.01
TypeofContact_Self Enquiry     NumberOfChildrenVisiting        -0.01
                               CityTier                        -0.01
MaritalStatus_Unmarried        DurationOfPitch                 -0.01
MaritalStatus_Married          OwnCar                          -0.01
MaritalStatus_Single           TypeofContact_Self Enquiry      -0.01
PitchSatisfactionScore         NumberOfTrips                   -0.01
TypeofContact_Self Enquiry     NumberOfPersonVisiting          -0.01
DurationOfPitch                Age                             -0.01
Occupation_Salaried            NumberOfFollowups               -0.01
                               Age                             -0.01
ProductPitched_King            Occupation_Salaried             -0.01
Occupation_Salaried            Gender                          -0.01
Occupation_Large Business      Occupation_Free Lancer          -0.01
MaritalStatus_Unmarried        PreferredPropertyStar           -0.01
MaritalStatus_Single           Occupation_Small Business       -0.01
Gender                         DurationOfPitch                 -0.01
MaritalStatus_Divorced         Passport                        -0.01
Occupation_Small Business      NumberOfTrips                   -0.01
TypeofContact_Self Enquiry     Gender                          -0.01
MaritalStatus_Married          TypeofContact_Self Enquiry      -0.01
ProductPitched_Super Deluxe    Occupation_Free Lancer          -0.01
PreferredPropertyStar          DurationOfPitch                 -0.01
MaritalStatus_Married          Occupation_Salaried             -0.00
TypeofContact_Company Invited  MonthlyIncome                   -0.00
MaritalStatus_Divorced         Occupation_Large Business       -0.00
ProductPitched_Super Deluxe    TypeofContact_Self Enquiry      -0.00
ProductPitched_King            Occupation_Free Lancer          -0.00
MaritalStatus_Divorced         PreferredPropertyStar           -0.00
Occupation_Large Business      NumberOfPersonVisiting          -0.00
TypeofContact_Company Invited  OwnCar                          -0.00
ProductPitched_Standard        TypeofContact_Self Enquiry      -0.00
                               PitchSatisfactionScore          -0.00
Occupation_Salaried            NumberOfPersonVisiting          -0.00
Occupation_Free Lancer         PitchSatisfactionScore          -0.00
Occupation_Large Business      Passport                        -0.00
MaritalStatus_Divorced         Occupation_Small Business       -0.00
TypeofContact_Self Enquiry     Passport                        -0.00
MaritalStatus_Divorced         Gender                          -0.00
MaritalStatus_Unmarried        Occupation_Large Business        0.00
OwnCar                         DurationOfPitch                  0.00
NumberOfChildrenVisiting       PitchSatisfactionScore           0.00
Occupation_Free Lancer         Age                              0.00
TypeofContact_Company Invited  Passport                         0.00
NumberOfTrips                  Gender                           0.00
Passport                       NumberOfFollowups                0.00
                               PreferredPropertyStar            0.00
NumberOfPersonVisiting         CityTier                         0.00
PitchSatisfactionScore         Gender                           0.00
MaritalStatus_Married          Passport                         0.00
Designation_Manager            MaritalStatus_Divorced           0.00
MaritalStatus_Divorced         ProductPitched_Deluxe            0.00
                               ProductPitched_Standard          0.00
Designation_Senior Manager     MaritalStatus_Divorced           0.00
MonthlyIncome                  Passport                         0.00
                               PreferredPropertyStar            0.00
ProductPitched_Basic           NumberOfFollowups                0.00
NumberOfChildrenVisiting       CityTier                         0.00
Occupation_Small Business      NumberOfChildrenVisiting         0.00
ProductPitched_Standard        TypeofContact_Company Invited    0.00
ProductPitched_Basic           PreferredPropertyStar            0.00
TypeofContact_Self Enquiry     OwnCar                           0.00
Occupation_Small Business      NumberOfPersonVisiting           0.00
MaritalStatus_Divorced         Occupation_Salaried              0.00
NumberOfPersonVisiting         ProdTaken                        0.00
ProductPitched_Super Deluxe    TypeofContact_Company Invited    0.00
                               Occupation_Salaried              0.00
Occupation_Large Business      Gender                           0.00
TypeofContact_Self Enquiry     MonthlyIncome                    0.00
NumberOfFollowups              Gender                           0.00
ProductPitched_Basic           PitchSatisfactionScore           0.00
Passport                       CityTier                         0.00
CityTier                       Age                              0.01
PitchSatisfactionScore         NumberOfFollowups                0.01
Occupation_Small Business      Gender                           0.01
MaritalStatus_Married          TypeofContact_Company Invited    0.01
Passport                       NumberOfPersonVisiting           0.01
TypeofContact_Company Invited  Gender                           0.01
NumberOfTrips                  PreferredPropertyStar            0.01
ProductPitched_Deluxe          Gender                           0.01
ProductPitched_Standard        Occupation_Small Business        0.01
Designation_AVP                MaritalStatus_Married            0.01
MaritalStatus_Married          ProductPitched_Super Deluxe      0.01
TypeofContact_Company Invited  NumberOfPersonVisiting           0.01
MaritalStatus_Divorced         CityTier                         0.01
MaritalStatus_Single           TypeofContact_Company Invited    0.01
Passport                       NumberOfTrips                    0.01
NumberOfTrips                  ProdTaken                        0.01
DurationOfPitch                CityTier                         0.01
TypeofContact_Company Invited  CityTier                         0.01
                               NumberOfChildrenVisiting         0.01
NumberOfChildrenVisiting       ProdTaken                        0.01
Occupation_Small Business      Passport                         0.01
MaritalStatus_Unmarried        NumberOfTrips                    0.01
ProductPitched_Standard        NumberOfTrips                    0.01
OwnCar                         NumberOfPersonVisiting           0.01
PitchSatisfactionScore         Passport                         0.01
OwnCar                         NumberOfFollowups                0.01
ProductPitched_Super Deluxe    Occupation_Small Business        0.01
TypeofContact_Self Enquiry     Age                              0.01
MaritalStatus_Divorced         NumberOfTrips                    0.01
Occupation_Large Business      PreferredPropertyStar            0.01
MaritalStatus_Unmarried        OwnCar                           0.01
TypeofContact_Self Enquiry     NumberOfTrips                    0.01
Occupation_Small Business      TypeofContact_Self Enquiry       0.01
NumberOfTrips                  DurationOfPitch                  0.01
PitchSatisfactionScore         DurationOfPitch                  0.01
OwnCar                         CityTier                         0.01
MaritalStatus_Married          ProductPitched_King              0.01
Designation_VP                 MaritalStatus_Married            0.01
MaritalStatus_Divorced         ProductPitched_King              0.01
Designation_VP                 MaritalStatus_Divorced           0.01
PitchSatisfactionScore         Age                              0.01
Occupation_Large Business      NumberOfChildrenVisiting         0.01
MaritalStatus_Divorced         DurationOfPitch                  0.01
MaritalStatus_Unmarried        Occupation_Salaried              0.01
Occupation_Free Lancer         TypeofContact_Self Enquiry       0.01
NumberOfPersonVisiting         Gender                           0.01
OwnCar                         Gender                           0.01
ProductPitched_Basic           TypeofContact_Self Enquiry       0.02
MaritalStatus_Married          PreferredPropertyStar            0.02
ProductPitched_Super Deluxe    Passport                         0.02
MaritalStatus_Married          Occupation_Small Business        0.02
OwnCar                         PreferredPropertyStar            0.02
NumberOfChildrenVisiting       Passport                         0.02
NumberOfFollowups              DurationOfPitch                  0.02
Occupation_Free Lancer         NumberOfFollowups                0.02
MaritalStatus_Single           PitchSatisfactionScore           0.02
Occupation_Free Lancer         NumberOfPersonVisiting           0.02
MaritalStatus_Married          NumberOfChildrenVisiting         0.02
TypeofContact_Company Invited  DurationOfPitch                  0.02
Gender                         CityTier                         0.02
MaritalStatus_Unmarried        TypeofContact_Company Invited    0.02
ProductPitched_King            Occupation_Small Business        0.02
                               PitchSatisfactionScore           0.02
Occupation_Small Business      Age                              0.02
                               PreferredPropertyStar            0.02
MaritalStatus_Married          NumberOfPersonVisiting           0.02
MaritalStatus_Single           Gender                           0.02
ProductPitched_Basic           Passport                         0.02
ProductPitched_Standard        DurationOfPitch                  0.02
Occupation_Salaried            OwnCar                           0.02
                               PitchSatisfactionScore           0.02
ProductPitched_Deluxe          TypeofContact_Company Invited    0.02
ProductPitched_Basic           NumberOfChildrenVisiting         0.02
MaritalStatus_Divorced         OwnCar                           0.02
TypeofContact_Company Invited  PitchSatisfactionScore           0.02
                               NumberOfFollowups                0.02
ProductPitched_Standard        NumberOfFollowups                0.02
ProductPitched_Super Deluxe    DurationOfPitch                  0.02
MonthlyIncome                  PitchSatisfactionScore           0.02
Designation_Manager            MaritalStatus_Married            0.02
MaritalStatus_Married          ProductPitched_Deluxe            0.02
MaritalStatus_Divorced         NumberOfPersonVisiting           0.03
NumberOfFollowups              CityTier                         0.03
ProductPitched_Super Deluxe    CityTier                         0.03
Passport                       Age                              0.03
ProductPitched_Basic           Occupation_Free Lancer           0.03
MaritalStatus_Married          ProductPitched_Standard          0.03
Designation_Senior Manager     MaritalStatus_Married            0.03
MaritalStatus_Married          DurationOfPitch                  0.03
Occupation_Large Business      NumberOfTrips                    0.03
                               NumberOfFollowups                0.03
NumberOfChildrenVisiting       OwnCar                           0.03
MaritalStatus_Single           Occupation_Large Business        0.03
Passport                       Gender                           0.03
Occupation_Large Business      OwnCar                           0.03
ProductPitched_King            OwnCar                           0.03
PreferredPropertyStar          Gender                           0.03
Gender                         Age                              0.03
MaritalStatus_Single           Passport                         0.03
ProductPitched_Super Deluxe    NumberOfTrips                    0.03
MaritalStatus_Divorced         ProductPitched_Super Deluxe      0.03
TypeofContact_Self Enquiry     PreferredPropertyStar            0.03
ProductPitched_King            TypeofContact_Self Enquiry       0.03
MaritalStatus_Divorced         PitchSatisfactionScore           0.03
                               TypeofContact_Self Enquiry       0.03
ProductPitched_Standard        Gender                           0.03
ProductPitched_Deluxe          Age                              0.03
MaritalStatus_Divorced         MonthlyIncome                    0.03
Occupation_Free Lancer         Passport                         0.03
Occupation_Salaried            TypeofContact_Company Invited    0.03
ProductPitched_Basic           Occupation_Large Business        0.03
ProductPitched_Standard        Occupation_Large Business        0.03
MonthlyIncome                  DurationOfPitch                  0.03
ProductPitched_Super Deluxe    Gender                           0.04
Occupation_Small Business      MonthlyIncome                    0.04
MaritalStatus_Unmarried        Gender                           0.04
Occupation_Large Business      TypeofContact_Self Enquiry       0.04
OwnCar                         Age                              0.04
MaritalStatus_Married          MonthlyIncome                    0.04
NumberOfChildrenVisiting       DurationOfPitch                  0.04
ProductPitched_Standard        OwnCar                           0.04
Passport                       DurationOfPitch                  0.04
ProductPitched_Deluxe          DurationOfPitch                  0.04
                               NumberOfFollowups                0.04
ProductPitched_Basic           NumberOfPersonVisiting           0.04
MonthlyIncome                  Gender                           0.04
ProductPitched_Standard        PreferredPropertyStar            0.04
PreferredPropertyStar          NumberOfPersonVisiting           0.04
NumberOfChildrenVisiting       PreferredPropertyStar            0.04
Occupation_Large Business      PitchSatisfactionScore           0.04
MaritalStatus_Single           ProductPitched_King              0.04
Designation_VP                 MaritalStatus_Single             0.04
ProductPitched_Basic           Occupation_Salaried              0.04
MaritalStatus_Married          NumberOfTrips                    0.04
Occupation_Free Lancer         ProdTaken                        0.05
MaritalStatus_Divorced         Age                              0.05
ProductPitched_Super Deluxe    OwnCar                           0.05
Occupation_Free Lancer         NumberOfTrips                    0.05
ProductPitched_Deluxe          Occupation_Small Business        0.05
MaritalStatus_Single           Occupation_Free Lancer           0.05
PitchSatisfactionScore         ProdTaken                        0.05
ProductPitched_Deluxe          NumberOfTrips                    0.05
MaritalStatus_Single           ProductPitched_Super Deluxe      0.05
Designation_AVP                MaritalStatus_Single             0.05
TypeofContact_Company Invited  ProdTaken                        0.06
MaritalStatus_Unmarried        ProdTaken                        0.06
ProductPitched_Super Deluxe    PitchSatisfactionScore           0.06
ProductPitched_Deluxe          NumberOfChildrenVisiting         0.06
ProductPitched_Standard        CityTier                         0.07
Occupation_Small Business      DurationOfPitch                  0.07
ProductPitched_Deluxe          NumberOfPersonVisiting           0.07
MaritalStatus_Married          Age                              0.07
MonthlyIncome                  OwnCar                           0.07
OwnCar                         PitchSatisfactionScore           0.07
MaritalStatus_Unmarried        CityTier                         0.08
                               MonthlyIncome                    0.08
NumberOfPersonVisiting         DurationOfPitch                  0.08
Occupation_Large Business      ProdTaken                        0.08
MaritalStatus_Unmarried        ProductPitched_Standard          0.08
DurationOfPitch                ProdTaken                        0.09
MaritalStatus_Unmarried        NumberOfChildrenVisiting         0.09
                               NumberOfPersonVisiting           0.09
CityTier                       ProdTaken                        0.09
PreferredPropertyStar          ProdTaken                        0.10
MonthlyIncome                  CityTier                         0.10
NumberOfFollowups              ProdTaken                        0.11
MaritalStatus_Unmarried        NumberOfFollowups                0.12
                               ProductPitched_Deluxe            0.12
Designation_Manager            MaritalStatus_Unmarried          0.12
NumberOfTrips                  NumberOfFollowups                0.13
MonthlyIncome                  NumberOfTrips                    0.14
                               NumberOfFollowups                0.15
                               NumberOfPersonVisiting           0.16
Occupation_Small Business      CityTier                         0.16
MonthlyIncome                  NumberOfChildrenVisiting         0.16
ProductPitched_Standard        Age                              0.17
ProductPitched_King            Age                              0.17
NumberOfChildrenVisiting       NumberOfTrips                    0.17
NumberOfTrips                  Age                              0.19
                               NumberOfPersonVisiting           0.19
MaritalStatus_Single           ProdTaken                        0.19
Designation_Executive          MaritalStatus_Single             0.21
MaritalStatus_Single           ProductPitched_Basic             0.21
ProductPitched_Deluxe          CityTier                         0.22
ProductPitched_Basic           ProdTaken                        0.22
Passport                       ProdTaken                        0.27
NumberOfChildrenVisiting       NumberOfFollowups                0.28
ProductPitched_Super Deluxe    Age                              0.31
NumberOfFollowups              NumberOfPersonVisiting           0.32
ProductPitched_King            MonthlyIncome                    0.34
ProductPitched_Standard        MonthlyIncome                    0.40
MonthlyIncome                  Age                              0.43
ProductPitched_Super Deluxe    MonthlyIncome                    0.47
NumberOfChildrenVisiting       NumberOfPersonVisiting           0.60
dtype: float64

# Libraries to build decision tree classifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

# To tune different models
from sklearn.model_selection import GridSearchCV

# To get diferent metric scores
from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
#     plot_confusion_matrix,
    make_scorer,
)

X = df.drop(["ProdTaken"], axis=1)
y = df["ProdTaken"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)

print("Number of rows in train data =", X_train.shape[0])
print("Number of rows in test data =", X_test.shape[0])

Number of rows in train data = 2889
Number of rows in test data = 1239

print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Percentage of classes in training set:
0   0.81
1   0.19
Name: ProdTaken, dtype: float64
Percentage of classes in test set:
0   0.80
1   0.20
Name: ProdTaken, dtype: float64

# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf


def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")

####Building classifier

model = DecisionTreeClassifier(criterion="gini", random_state=1)
model.fit(X_train, y_train)

DecisionTreeClassifier(random_state=1)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

######. Creating a decision tree model

decision_tree_perf_train = model_performance_classification_sklearn(
    model, X_train, y_train
)
decision_tree_perf_train

	Accuracy	Recall	Precision	F1
0	1.00	1.00	1.00	1.00

confusion_matrix_sklearn(model, X_train, y_train)

decision_tree_perf_test = model_performance_classification_sklearn(
    model, X_test, y_test
)
decision_tree_perf_test

	Accuracy	Recall	Precision	F1
0	0.90	0.76	0.76	0.76

confusion_matrix_sklearn(model, X_test, y_test)

column_names = list(X.columns)
feature_names = column_names
print(feature_names)

['Age', 'CityTier', 'DurationOfPitch', 'Gender', 'NumberOfPersonVisiting', 'NumberOfFollowups', 'PreferredPropertyStar', 'NumberOfTrips', 'Passport', 'PitchSatisfactionScore', 'OwnCar', 'NumberOfChildrenVisiting', 'MonthlyIncome', 'TypeofContact_Company Invited', 'TypeofContact_Self Enquiry', 'Occupation_Free Lancer', 'Occupation_Large Business', 'Occupation_Salaried', 'Occupation_Small Business', 'ProductPitched_Basic', 'ProductPitched_Deluxe', 'ProductPitched_King', 'ProductPitched_Standard', 'ProductPitched_Super Deluxe', 'MaritalStatus_Divorced', 'MaritalStatus_Married', 'MaritalStatus_Single', 'MaritalStatus_Unmarried', 'Designation_AVP', 'Designation_Executive', 'Designation_Manager', 'Designation_Senior Manager', 'Designation_VP']

plt.figure(figsize=(20, 30))

out = tree.plot_tree(
    model,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=True,
    class_names=True,
)
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()

importances = model.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

####Hyperparameter tuning on Decision Tree

# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1)

# Grid of parameters to choose from

parameters = {
    "max_depth": [np.arange(2, 50, 5), None],
    "criterion": ["entropy", "gini"],
    "splitter": ["best", "random"],
    "min_impurity_decrease": [0.000001, 0.00001, 0.0001],
}

# Type of scoring used to compare parameter combinations
acc_scorer = make_scorer(recall_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_

# Fit the best algorithm to the data.
estimator.fit(X_train, y_train)

DecisionTreeClassifier(min_impurity_decrease=1e-06, random_state=1)

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decision_tree_tune_perf_train = model_performance_classification_sklearn(
    estimator, X_train, y_train
)
decision_tree_tune_perf_train

	Accuracy	Recall	Precision	F1
0	1.00	1.00	1.00	1.00

confusion_matrix_sklearn(estimator, X_train, y_train)

decision_tree_tune_perf_test = model_performance_classification_sklearn(
    estimator, X_test, y_test
)

decision_tree_tune_perf_test

	Accuracy	Recall	Precision	F1
0	0.90	0.76	0.76	0.76

confusion_matrix_sklearn(estimator, X_test, y_test)

plt.figure(figsize=(15, 12))

tree.plot_tree(
    estimator,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=True,
    class_names=True,
)
plt.show()

clf = DecisionTreeClassifier(random_state=1)
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = path.ccp_alphas, path.impurities

pd.DataFrame(path)

	ccp_alphas	impurities
0	0.00	0.00
1	0.00	0.00
2	0.00	0.00
3	0.00	0.00
4	0.00	0.00
5	0.00	0.00
6	0.00	0.00
7	0.00	0.00
8	0.00	0.01
9	0.00	0.01
10	0.00	0.01
11	0.00	0.01
12	0.00	0.01
13	0.00	0.01
14	0.00	0.01
15	0.00	0.01
16	0.00	0.01
17	0.00	0.01
18	0.00	0.01
19	0.00	0.01
20	0.00	0.01
21	0.00	0.02
22	0.00	0.02
23	0.00	0.02
24	0.00	0.02
25	0.00	0.02
26	0.00	0.02
27	0.00	0.02
28	0.00	0.02
29	0.00	0.02
30	0.00	0.02
31	0.00	0.02
32	0.00	0.02
33	0.00	0.03
34	0.00	0.03
35	0.00	0.03
36	0.00	0.03
37	0.00	0.03
38	0.00	0.03
39	0.00	0.03
40	0.00	0.03
41	0.00	0.03
42	0.00	0.03
43	0.00	0.03
44	0.00	0.04
45	0.00	0.04
46	0.00	0.04
47	0.00	0.04
48	0.00	0.04
49	0.00	0.04
50	0.00	0.04
51	0.00	0.04
52	0.00	0.04
53	0.00	0.04
54	0.00	0.04
55	0.00	0.04
56	0.00	0.04
57	0.00	0.04
58	0.00	0.04
59	0.00	0.05
60	0.00	0.05
61	0.00	0.05
62	0.00	0.05
63	0.00	0.05
64	0.00	0.05
65	0.00	0.05
66	0.00	0.05
67	0.00	0.05
68	0.00	0.05
69	0.00	0.05
70	0.00	0.05
71	0.00	0.05
72	0.00	0.05
73	0.00	0.05
74	0.00	0.06
75	0.00	0.06
76	0.00	0.06
77	0.00	0.06
78	0.00	0.06
79	0.00	0.06
80	0.00	0.06
81	0.00	0.06
82	0.00	0.06
83	0.00	0.06
84	0.00	0.06
85	0.00	0.07
86	0.00	0.07
87	0.00	0.07
88	0.00	0.07
89	0.00	0.07
90	0.00	0.07
91	0.00	0.08
92	0.00	0.08
93	0.00	0.08
94	0.00	0.08
95	0.00	0.08
96	0.00	0.09
97	0.00	0.09
98	0.00	0.09
99	0.00	0.09
100	0.00	0.09
101	0.00	0.09
102	0.00	0.10
103	0.00	0.10
104	0.00	0.10
105	0.00	0.10
106	0.00	0.10
107	0.00	0.10
108	0.00	0.10
109	0.00	0.10
110	0.00	0.11
111	0.00	0.11
112	0.00	0.11
113	0.00	0.12
114	0.00	0.12
115	0.00	0.12
116	0.00	0.12
117	0.00	0.12
118	0.00	0.12
119	0.00	0.12
120	0.00	0.13
121	0.00	0.13
122	0.00	0.14
123	0.00	0.14
124	0.00	0.14
125	0.00	0.14
126	0.00	0.14
127	0.00	0.14
128	0.00	0.15
129	0.00	0.15
130	0.00	0.15
131	0.00	0.16
132	0.00	0.16
133	0.00	0.16
134	0.00	0.16
135	0.00	0.16
136	0.00	0.17
137	0.00	0.18
138	0.00	0.19
139	0.00	0.20
140	0.00	0.20
141	0.00	0.20
142	0.00	0.21
143	0.00	0.21
144	0.00	0.21
145	0.00	0.22
146	0.00	0.22
147	0.00	0.23
148	0.00	0.23
149	0.00	0.24
150	0.00	0.24
151	0.00	0.24
152	0.00	0.25
153	0.00	0.25
154	0.00	0.25
155	0.01	0.27
156	0.02	0.29
157	0.02	0.31

fig, ax = plt.subplots(figsize=(15, 5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()

clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(random_state=1, ccp_alpha=ccp_alpha)
    clf.fit(X_train, y_train)
    clfs.append(clf)
print(
    "Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
        clfs[-1].tree_.node_count, ccp_alphas[-1]
    )
)

Number of nodes in the last tree is: 1 with ccp_alpha: 0.021601827331998336

clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()

recall_train = []
for clf in clfs:
    pred_train = clf.predict(X_train)
    values_train = recall_score(y_train, pred_train)
    recall_train.append(values_train)

recall_test = []
for clf in clfs:
    pred_test = clf.predict(X_test)
    values_test = recall_score(y_test, pred_test)
    recall_test.append(values_test)

fig, ax = plt.subplots(figsize=(15, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("Recall")
ax.set_title("Recall vs alpha for training and testing sets")
ax.plot(ccp_alphas, recall_train, marker="o", label="train", drawstyle="steps-post")
ax.plot(ccp_alphas, recall_test, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()

decision_tree_postpruned_perf_train = model_performance_classification_sklearn(
    best_model, X_train, y_train
)
decision_tree_postpruned_perf_train

	Accuracy	Recall	Precision	F1
0	1.00	1.00	1.00	1.00

decision_tree_postpruned_perf_test = model_performance_classification_sklearn(
    best_model, X_test, y_test
)
decision_tree_postpruned_perf_test

	Accuracy	Recall	Precision	F1
0	0.90	0.76	0.76	0.76

# training performance comparison

models_train_comp_df = pd.concat(
    [
        decision_tree_perf_train.T,
        decision_tree_tune_perf_train.T,
        decision_tree_postpruned_perf_train.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Decision Tree sklearn",
    "Decision Tree (Pre-Pruning)",
    "Decision Tree (Post-Pruning)",
]
print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	Decision Tree sklearn	Decision Tree (Pre-Pruning)	Decision Tree (Post-Pruning)
Accuracy	1.00	1.00	1.00
Recall	1.00	1.00	1.00
Precision	1.00	1.00	1.00
F1	1.00	1.00	1.00

#Fitting the model
d_tree = DecisionTreeClassifier(random_state=1)
d_tree.fit(X_train,y_train)

#Calculating different metrics
d_tree_model_train_perf=model_performance_classification_sklearn(d_tree,X_train,y_train)
print("Training performance:\n",d_tree_model_train_perf)
d_tree_model_test_perf=model_performance_classification_sklearn(d_tree,X_test,y_test)
print("Testing performance:\n",d_tree_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(d_tree,X_test,y_test)

Training performance:
    Accuracy  Recall  Precision   F1
0      1.00    1.00       1.00 1.00
Testing performance:
    Accuracy  Recall  Precision   F1
0      0.90    0.76       0.76 0.76

######. Building a Random Forrest Model

#Fitting the model
rf_estimator = RandomForestClassifier(random_state=1)
rf_estimator.fit(X_train,y_train)

#Calculating different metrics
rf_estimator_model_train_perf=model_performance_classification_sklearn(rf_estimator,X_train,y_train)
print("Training performance:\n",rf_estimator_model_train_perf)
rf_estimator_model_test_perf=model_performance_classification_sklearn(rf_estimator,X_test,y_test)
print("Testing performance:\n",rf_estimator_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(rf_estimator,X_test,y_test)

Training performance:
    Accuracy  Recall  Precision   F1
0      1.00    1.00       1.00 1.00
Testing performance:
    Accuracy  Recall  Precision   F1
0      0.91    0.57       0.95 0.71

# Choose the type of classifier. 
rf_tuned = RandomForestClassifier(class_weight={0:0.18,1:0.82},random_state=1,oob_score=True,bootstrap=True)

parameters = {  
                'max_depth': list(np.arange(5,30,5)) + [None]
     ,
                 'max_features': ['sqrt','log2',None]
    ,
                 'min_samples_leaf': np.arange(1,15,5)
    ,
                'min_samples_split': np.arange(2, 20, 5)}


# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.f1_score)

# Run the grid search
grid_obj = GridSearchCV(rf_tuned, parameters, scoring=scorer, cv=5,n_jobs=-1)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
rf_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data. 
rf_tuned.fit(X_train, y_train)

RandomForestClassifier(class_weight={0: 0.18, 1: 0.82}, max_depth=20,
                       max_features=None, min_samples_split=7, oob_score=True,
                       random_state=1)

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#Calculating different metrics
rf_tuned_model_train_perf=model_performance_classification_sklearn(rf_tuned,X_train,y_train)
print("Training performance:\n",rf_tuned_model_train_perf)
rf_tuned_model_test_perf=model_performance_classification_sklearn(rf_tuned,X_test,y_test)
print("Testing performance:\n",rf_tuned_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(rf_tuned,X_test,y_test)

Training performance:
    Accuracy  Recall  Precision   F1
0      1.00    0.99       0.98 0.99
Testing performance:
    Accuracy  Recall  Precision   F1
0      0.90    0.65       0.83 0.73

# Define the parameter grid for hyperparameter tuning
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 5, 10],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4],
    'max_features': ['auto', 'sqrt'],
    'bootstrap': [True, False],
    'min_weight_fraction_leaf': [0.0, 0.1, 0.2]
}

# Create a random forest classifier
rf = RandomForestClassifier()

# Perform grid search cross-validation
grid_search = GridSearchCV(rf, param_grid, cv=5)
grid_search.fit(X_train, y_train)

# Print the best parameters and best score
print("Best Parameters: ", grid_search.best_params_)
print("Best Score: ", grid_search.best_score_)

# Evaluate the best model on the test set
best_model = grid_search.best_estimator_

# Get the best decision tree estimator
dtree_estimator = grid_search.best_estimator_
test_accuracy = best_model.score(X_test, y_test)
print("Test Accuracy: ", test_accuracy)

Best Parameters:  {'bootstrap': False, 'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100}
Best Score:  0.9155409497880098
Test Accuracy:  0.9289749798224375

DecisionTreeClassifier(class_weight={0: 0.18, 1: 0.72}, random_state=1)

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#Calculating different metrics
dtree_estimator_model_train_perf=model_performance_classification_sklearn(dtree_estimator,X_train,y_train)
print("Training performance:\n",dtree_estimator_model_train_perf)
dtree_estimator_model_test_perf=model_performance_classification_sklearn(dtree_estimator,X_test,y_test)
print("Testing performance:\n",dtree_estimator_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(dtree_estimator,X_test,y_test)

Training performance:
    Accuracy  Recall  Precision   F1
0      1.00    1.00       1.00 1.00
Testing performance:
    Accuracy  Recall  Precision   F1
0      0.88    0.70       0.70 0.70

Observations

1. The decisions tree was clearly overfit. When we used the test model the accurakcy was only about 76%, which isn't terrible, but not great
2. Using Random Forrest produced much more reliable results. While the training data. set was 99% which is pretty high, and possbily over fit, the Accuracy is 92% which is far better than the decision tree.

# Choose the type of classifier. 
bagging_estimator_tuned = BaggingClassifier(random_state=1)

# Grid of parameters to choose from
parameters = {'max_samples': [0.7,0.8,0.9,1], 
              'max_features': [0.7,0.8,0.9,1],
              'n_estimators' : [10,20,30,40,50],
             }

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.f1_score)

# Run the grid search
grid_obj = GridSearchCV(bagging_estimator_tuned, parameters, scoring=scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
bagging_estimator_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
bagging_estimator_tuned.fit(X_train, y_train)

BaggingClassifier(max_features=0.9, max_samples=0.9, n_estimators=50,
                  random_state=1)

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#Fitting the model
bagging_classifier = BaggingClassifier(random_state=1)
bagging_classifier.fit(X_train,y_train)

#Calculating different metrics
bagging_classifier_model_train_perf=model_performance_classification_sklearn(bagging_classifier,X_train,y_train)
print(bagging_classifier_model_train_perf)
bagging_classifier_model_test_perf=model_performance_classification_sklearn(bagging_classifier,X_test,y_test)
print(bagging_classifier_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(bagging_classifier,X_test,y_test)

   Accuracy  Recall  Precision   F1
0      0.99    0.97       1.00 0.98
   Accuracy  Recall  Precision   F1
0      0.91    0.59       0.91 0.72

#Calculating different metrics
bagging_estimator_tuned_model_train_perf=model_performance_classification_sklearn(bagging_estimator_tuned,X_train,y_train)
print(bagging_estimator_tuned_model_train_perf)
bagging_estimator_tuned_model_test_perf=model_performance_classification_sklearn(bagging_estimator_tuned,X_test,y_test)
print(bagging_estimator_tuned_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(bagging_estimator_tuned,X_test,y_test)

   Accuracy  Recall  Precision   F1
0      1.00    1.00       1.00 1.00
   Accuracy  Recall  Precision   F1
0      0.92    0.64       0.95 0.77

#Fitting the model -- ADABOOOST
ab_classifier = AdaBoostClassifier(random_state=1)
ab_classifier.fit(X_train,y_train)

#Calculating different metrics
ab_classifier_model_train_perf=model_performance_classification_sklearn(ab_classifier,X_train,y_train)
print(ab_classifier_model_train_perf)
ab_classifier_model_test_perf=model_performance_classification_sklearn(ab_classifier,X_test,y_test)
print(ab_classifier_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(ab_classifier,X_test,y_test)

   Accuracy  Recall  Precision   F1
0      0.85    0.36       0.71 0.48
   Accuracy  Recall  Precision   F1
0      0.84    0.37       0.70 0.48

# Choose the type of classifier. 
bagging_estimator_tuned = BaggingClassifier(random_state=1)

# Grid of parameters to choose from
parameters = {  'max_samples': [0.7,0.8,0.9,1], 
              'max_features': [0.7,0.8,0.9,1],
              'n_estimators' : [10,20,30,40,50]
             }

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.f1_score)

# Run the grid search
grid_obj = GridSearchCV(bagging_estimator_tuned, parameters, scoring=scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
bagging_estimator_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
bagging_estimator_tuned.fit(X_train, y_train)

BaggingClassifier(max_features=0.9, max_samples=0.9, n_estimators=50,
                  random_state=1)

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#Calculating different metrics
abc_tuned_model_train_perf=model_performance_classification_sklearn(abc_tuned,X_train,y_train)
print(abc_tuned_model_train_perf)
abc_tuned_model_test_perf=model_performance_classification_sklearn(abc_tuned,X_test,y_test)
print(abc_tuned_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(abc_tuned,X_test,y_test)

   Accuracy  Recall  Precision   F1
0      1.00    1.00       1.00 1.00
   Accuracy  Recall  Precision   F1
0      0.90    0.64       0.81 0.71

#Fitting the model
gb_classifier = GradientBoostingClassifier(random_state=1)
gb_classifier.fit(X_train,y_train)

#Calculating different metrics
gb_classifier_model_train_perf=model_performance_classification_sklearn(gb_classifier,X_train,y_train)
print("Training performance:\n",gb_classifier_model_train_perf)
gb_classifier_model_test_perf=model_performance_classification_sklearn(gb_classifier,X_test,y_test)
print("Testing performance:\n",gb_classifier_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(gb_classifier,X_test,y_test)

Training performance:
    Accuracy  Recall  Precision   F1
0      0.90    0.51       0.90 0.65
Testing performance:
    Accuracy  Recall  Precision   F1
0      0.86    0.39       0.79 0.52

# Choose the type of classifier. 
gbc_tuned = GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),random_state=1)

# Grid of parameters to choose from
parameters = {
    "n_estimators": [100,150,200,250],
    "subsample":[0.8,0.9,1],
    "max_features":[0.7,0.8,0.9,1]
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.f1_score)

# Run the grid search
grid_obj = GridSearchCV(gbc_tuned, parameters, scoring=scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
gbc_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
gbc_tuned.fit(X_train, y_train)

GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),
                           max_features=0.9, n_estimators=250, random_state=1,
                           subsample=0.8)

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#Calculating different metrics
gbc_tuned_model_train_perf=model_performance_classification_sklearn(gbc_tuned,X_train,y_train)
print("Training performance:\n",gbc_tuned_model_train_perf)
gbc_tuned_model_test_perf=model_performance_classification_sklearn(gbc_tuned,X_test,y_test)
print("Testing performance:\n",gbc_tuned_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(gbc_tuned,X_test,y_test)

Training performance:
    Accuracy  Recall  Precision   F1
0      0.93    0.70       0.95 0.80
Testing performance:
    Accuracy  Recall  Precision   F1
0      0.88    0.49       0.82 0.61

#Fitting the model
xgb_classifier = XGBClassifier(random_state=1, eval_metric='logloss')
xgb_classifier.fit(X_train,y_train)

#Calculating different metrics
xgb_classifier_model_train_perf=model_performance_classification_sklearn(xgb_classifier,X_train,y_train)
print("Training performance:\n",xgb_classifier_model_train_perf)
xgb_classifier_model_test_perf=model_performance_classification_sklearn(xgb_classifier,X_test,y_test)
print("Testing performance:\n",xgb_classifier_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(xgb_classifier,X_test,y_test)

Training performance:
    Accuracy  Recall  Precision   F1
0      1.00    1.00       1.00 1.00
Testing performance:
    Accuracy  Recall  Precision   F1
0      0.93    0.69       0.95 0.80

# Choose the type of classifier. 
xgb_tuned = XGBClassifier(random_state=1, eval_metric='logloss')

# Grid of parameters to choose from
parameters = {
    "n_estimators": [10,30,50],
    "scale_pos_weight":[1,2,5],
    "subsample":[0.7,0.9,1],
    "learning_rate":[0.05, 0.1,0.2],
    "colsample_bytree":[0.7,0.9,1],
    "colsample_bylevel":[0.5,0.7,1]
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.f1_score)

# Run the grid search
grid_obj = GridSearchCV(xgb_tuned, parameters,scoring=scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
xgb_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
xgb_tuned.fit(X_train, y_train)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=1, colsample_bynode=None, colsample_bytree=1,
              early_stopping_rounds=None, enable_categorical=False,
              eval_metric='logloss', feature_types=None, gamma=None,
              gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=0.2, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=50, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=1, ...)

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#Calculating different metrics
xgb_tuned_model_train_perf=model_performance_classification_sklearn(xgb_tuned,X_train,y_train)
print("Training performance:\n",xgb_tuned_model_train_perf)
xgb_tuned_model_test_perf=model_performance_classification_sklearn(xgb_tuned,X_test,y_test)
print("Testing performance:\n",xgb_tuned_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(xgb_tuned,X_test,y_test)

Training performance:
    Accuracy  Recall  Precision   F1
0      0.99    1.00       0.95 0.98
Testing performance:
    Accuracy  Recall  Precision   F1
0      0.90    0.75       0.75 0.75

# Libraries to import decision tree classifier and different ensemble classifiers
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.tree import DecisionTreeClassifier

estimators = [('Random Forest',rf_tuned), ('Gradient Boosting',gbc_tuned), ('AdaBoost',abc_tuned)]

final_estimator = xgb_tuned

stacking_classifier= StackingClassifier(estimators=estimators,final_estimator=final_estimator)

stacking_classifier.fit(X_train,y_train)

StackingClassifier(estimators=[('Random Forest',
                                RandomForestClassifier(class_weight={0: 0.18,
                                                                     1: 0.82},
                                                       max_depth=20,
                                                       max_features=None,
                                                       min_samples_split=7,
                                                       oob_score=True,
                                                       random_state=1)),
                               ('Gradient Boosting',
                                GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),
                                                           max_features=0.9,
                                                           n_estimators=250,
                                                           random_state=1,
                                                           subsample=0.8)),
                               ('AdaBoost',
                                AdaB...
                                                 gpu_id=None, grow_policy=None,
                                                 importance_type=None,
                                                 interaction_constraints=None,
                                                 learning_rate=0.2,
                                                 max_bin=None,
                                                 max_cat_threshold=None,
                                                 max_cat_to_onehot=None,
                                                 max_delta_step=None,
                                                 max_depth=None,
                                                 max_leaves=None,
                                                 min_child_weight=None,
                                                 missing=nan,
                                                 monotone_constraints=None,
                                                 n_estimators=50, n_jobs=None,
                                                 num_parallel_tree=None,
                                                 predictor=None, random_state=1, ...))

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#Calculating different metrics
stacking_classifier_model_train_perf=model_performance_classification_sklearn(stacking_classifier,X_train,y_train)
print("Training performance:\n",stacking_classifier_model_train_perf)
stacking_classifier_model_test_perf=model_performance_classification_sklearn(stacking_classifier,X_test,y_test)
print("Testing performance:\n",stacking_classifier_model_test_perf)

#Creating confusion matrix
confusion_matrix_sklearn(stacking_classifier,X_test,y_test)

Training performance:
    Accuracy  Recall  Precision   F1
0      0.98    1.00       0.91 0.95
Testing performance:
    Accuracy  Recall  Precision   F1
0      0.90    0.82       0.73 0.78

# training performance comparison

models_train_comp_df = pd.concat(
    [
        d_tree_model_train_perf.T,
        dtree_estimator_model_train_perf.T,
        rf_estimator_model_train_perf.T,
        rf_tuned_model_train_perf.T,
     bagging_classifier_model_train_perf.T,
        bagging_estimator_tuned_model_train_perf.T,
        ab_classifier_model_train_perf.T,
     abc_tuned_model_train_perf.T,
        gb_classifier_model_train_perf.T,
        gbc_tuned_model_train_perf.
        T,xgb_classifier_model_train_perf.T,
    xgb_tuned_model_train_perf.T,
        stacking_classifier_model_train_perf.T],
    axis=1,
)
models_train_comp_df.columns = [
    "Decision Tree",
    "Decision Tree Estimator",
    "Random Forest Estimator",
    "Random Forest Tuned",
    "Bagging Classifier",
    "Bagging Estimator Tuned",
    "Adaboost Classifier",
    "Adabosst Classifier Tuned",
    "Gradient Boost Classifier",
    "Gradient Boost Classifier Tuned",
    "XGBoost Classifier",
    "XGBoost Classifier Tuned",
    "Stacking Classifier"]
print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	Decision Tree	Decision Tree Estimator	Random Forest Estimator	Random Forest Tuned	Bagging Classifier	Bagging Estimator Tuned	Adaboost Classifier	Adabosst Classifier Tuned	Gradient Boost Classifier	Gradient Boost Classifier Tuned	XGBoost Classifier	XGBoost Classifier Tuned	Stacking Classifier
Accuracy	1.00	1.00	1.00	1.00	0.99	1.00	0.85	1.00	0.90	0.93	1.00	0.99	0.98
Recall	1.00	1.00	1.00	0.99	0.97	1.00	0.36	1.00	0.51	0.70	1.00	1.00	1.00
Precision	1.00	1.00	1.00	0.98	1.00	1.00	0.71	1.00	0.90	0.95	1.00	0.95	0.91
F1	1.00	1.00	1.00	0.99	0.98	1.00	0.48	1.00	0.65	0.80	1.00	0.98	0.95

Conclusions

There are some really good models built here. Random Forrest, Bagging and XGBoost are pretty good, I would have to lean most towards Random Forest as a predictions model though.

feature_names = X_train.columns
importances = rf_tuned.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

df.groupby(['ProdTaken'])['Age'].mean()

ProdTaken
0   37.93
1   34.33
Name: Age, dtype: float64

df.groupby(['ProdTaken'])['DurationOfPitch'].mean()

ProdTaken
0   15.22
1   17.00
Name: DurationOfPitch, dtype: float64

df.groupby(['ProdTaken'])['MonthlyIncome'].mean()

ProdTaken
0   23378.20
1   21898.13
Name: MonthlyIncome, dtype: float64

print(df.groupby(['ProdTaken'])['PitchSatisfactionScore'].mean(),
      df.groupby(['ProdTaken'])['PitchSatisfactionScore'].min(),
      df.groupby(['ProdTaken'])['PitchSatisfactionScore'].max())
      

ProdTaken
0   3.03
1   3.21
Name: PitchSatisfactionScore, dtype: float64 ProdTaken
0    1
1    1
Name: PitchSatisfactionScore, dtype: int64 ProdTaken
0    5
1    5
Name: PitchSatisfactionScore, dtype: int64

Recomendations and Conclusions

1. The most valuable variable is the age, and it looks like around 34 years old is the most popular age to purchase trips.
2. The duration is the second most influential variable. Age and duration of presentation are the most impactful on weither soemone buys the trip or not. It looks like 17 minutes is the sweet spot for presentation. So if presentations are focused on people in their early 30's for about 17 minutes, looks like the most promising way to book a sale.
3. Monthly Income of course is a big contribputing factor to if someone books a sale or not. It looks like people that earn close to 22k per month book a trip, but that number is very close to the income people make that do not book a trip, so income needs to be evaluated further.
4. The fourth most influential variable is presentation satisfation, but it seems like both people that book and don't book a presentation both rate about a 3, which is an average presentation, so if the company focuses on the age and duration of pitch, they are most likely to see an increase in sales